MECHANICAL STRATEGIST

Algorithmic Philosophy

ALGORITHM

Short algorithms embodying explanatory and predictive powers are what we mean by knowledge and aim to discover in science. The fact that some of the known algorithms have never failed in their predictions is a strong indication that we live in an algorithmically deterministic world.

RICHNESS

Meta-system transitions enrich law-like algorithms into past-driven and end-directed by giving them states and the ability to simulate. These meta-algorithms emerge law-like by necessity, past-driven through evolution, and, eventually, end-directed by design.

PURPOSE

The ability to recursively self-simulate and become one of the simulated variations gives end-directed algorithms the only kind of free will there can be in a deterministic world. And the rich algorithms as such cannot fulfil their embedded goals without maintaining their algorithmic richness.

STRATEGY

Maintaining richness requires predictive and deontic powers. To optimize, I rely on scientific method, mechanism design and social contracts. And since emotions have been past-driven to help genes, not me, I have made an agreement with my simulated future selves to only enjoy pleasures that lead to no harm.

MODEL

Maximizing the area under the survival curve (S) requires an accurate (ROC) and short (K) model (A*) of algorithms (A) taking actions (r) from strategies available (R) based on their time-average exponential growth rate (ḡ). The less accurate the predictive model, the more uncertainty, and the more diverse (H) the set of strategies should be. Cooperation and diversification are good strategies, because many natural growth processes are not ergodic and ensemble-average is higher than time-average.

Discussion

SEARLE

The Chinese Room Argument for the syntax-semantics barrier commits the fallacy of composition, because law-like algorithms can be enriched to have meaning via meta-system transitions. The algorithm can thus have the ability to understand Chinese, whereas a static rulebook alone would not.

HUME

The Is-Ought Gap can only be found in bad arguments. Rich algorithms exist and to fulfil their goals, logically, ought to maintain their richness. Real moral problems arise when contractual mechanisms are not DSIC (dominant-strategy incentive-compatible).

BOSTROM

The Simulation Argument might be valid, but the hypothesis that we live in a simulation is probably not falsifiable. Nevertheless, if we do live in a simulation, the problem of evil suggests that our simulators are either indifferent, incompetent, or evil.

CARROLL

Poetic Naturalism argues that a thing is real, if its model is consistent and useful in a given context. My algorithmic philosophy takes the same approach. In algorithmic terms an algorithm exists on all levels of richness where its model has predictive or deontic power.